Next Article in Journal
Adherence to the CDK 4/6 Inhibitor Palbociclib and Omission of Dose Management Supported by Pharmacometric Modelling as Part of the OpTAT Study
Previous Article in Journal
Predictors of Symptomatic Venous Thromboembolism in Patients with Soft Tissue Sarcoma in the Lower Extremity
Previous Article in Special Issue
Renal Carcinoma and Angiogenesis: Therapeutic Target and Biomarkers of Response in Current Therapies
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Cancers
Volume 15
Issue 1
10.3390/cancers15010313
cancers-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Therapeutic Drug Monitoring of Tyrosine Kinase Inhibitors in the Treatment of Advanced Renal Cancer

by
Florent Puisset
1,2,
Mourad Mseddi
3,
Loïc Mourey
1,
Damien Pouessel
1,
Benoit Blanchet
3,4,
Etienne Chatelut
1,2,* and
Christine Chevreau
1
1
Institut Claudius-Regaud, Institut Universitaire du Cancer de Toulouse–Oncopole, 31059 Toulouse, France
2
CRCT, Cancer Research Center of Toulouse, Inserm U1037, Université Paul Sabatier, 31037 Toulouse, France
3
Department of Pharmacokinetics and Pharmacochemistry, Cochin University Hospital, Assistance Publique-Hôpitaux de Paris, CARPEM, 75014 Paris, France
4
UMR8038 CNRS, U1268 INSERM, Faculté de Pharmacie, Université Paris Cité, PRES Sorbonne Paris Cité, CARPEM, 75006 Paris, France
*
Author to whom correspondence should be addressed.
Cancers 2023, 15(1), 313; https://doi.org/10.3390/cancers15010313
Submission received: 1 December 2022 / Revised: 28 December 2022 / Accepted: 30 December 2022 / Published: 3 January 2023
(This article belongs to the Special Issue High Unmet Medical Needs in the Treatment of Renal Cell Carcinoma)
Browse Figure
Versions Notes

Abstract

:

Simple Summary

The rational, benefits and limits of therapeutic drug monitoring as a routine practice are discussed for the seven tyrosine kinase inhibitor compounds currently used to treat advanced renal cell carcinoma.

Abstract

Seven tyrosine kinase inhibitor compounds with anti-angiogenic properties remain key drugs to treat advanced renal cell carcinoma. There is a strong rationale to develop therapeutic drug monitoring for these drugs. General considerations of such monitoring of the several groups of anticancer drugs are given, with a focus on oral therapy. Pharmacokinetics and the factors of inter- and intraindividual variabilities of these tyrosine kinase inhibitors are described together with an exhaustive presentation of their pharmacokinetic/pharmacodynamic relationships. The latter was observed in studies where every patient was treated with the same dose, and the results of several prospective studies based on dose individualization support the practice of increasing individual dosage in case of low observed plasma drug concentrations. Finally, the benefits and limits of therapeutic drug monitoring as a routine practice are discussed.

1. Introduction

Seven tyrosine kinase inhibitor (TKI) compounds with anti-angiogenic properties associated with their affinity for the vascular endothelial growth factor receptor (VEGFR) are currently the key drugs to treat advanced renal cell carcinoma: cabozantinib, axitinib, sorafenib, sunitinib, pazopanib, lenvatinib, and tivozanib [1]. TKI monotherapy represents the first-choice therapy for treating non-clear cell renal cancer, and the first choice as an alternative therapy if immunotherapy is not tolerated or is inapplicable. TKIs are also combined with an immune check-point inhibitor (CPI) such as pembrolizumab, nivolumab, ipilimumab or avelumab [2,3,4,5,6]. In order to achieve optimal plasma exposure to TKIs, therapeutic drug monitoring (TDM) is now an added consideration which consists of determining plasma concentration in a specific patient to eventually adjust her/his dosage. This practice with TKIs was initially developed for imatinib to treat chronic myeloid leukemia. Trough plasma concentrations at steady-state lower than 1 mg/L have been seen to be associated with a higher risk of treatment failure [7]. Given these initial results, along with the strong rationale for TDM of other TKIs (see below), this practice has begun to be widely used for other TKIs in treating solid tumors including advanced renal cancer [8].

2. General Considerations for TDM of Anticancer Drugs

To date, TDM is not carried out for most anticancer drugs, unlike for other drugs such as immunosuppressants. Although close relationships have been described for cytotoxics between their area-under-the-plasma concentration vs. time curve (AUC) and their dose-limiting toxicity, solely determining their plasma concentrations would not allow physicians to adjust dosing for an on-going cycle since these drugs are administered on a Day 1 = Day 21 schedule. Thus, in current practice, cytotoxic dosage is only decreased for the following cycle in case of unacceptable toxicity. This is due to the fact that cytotoxic regimens are composed of two or three drugs, and that the AUC of each one requires multiple blood sampling. Implementation of TDM for cytotoxics is thus hampered [9]. In the case of monoclonal antibodies (Mab) regardless of their pharmacological group (targeted therapies or immune check-point inhibitors), TDM is not justified: no clear pharmacokinetic-pharmacodynamic (PK-PD) relationships have been demonstrated since their dosage is associated with plasma concentrations far higher than those required for activity [10].
Oral targeted therapies, however, corresponding to small molecules and, particularly, TKIs, present all the criteria required to consider TDM, i.e., large interindividual pharmacokinetics (PK) but limited intrapatient PK variability, a better relationship between plasma concentration and pharmacodynamics (PD) than between dosage and PD, long-term treatment, and substantial time lapse between the PD endpoint and the moment of measuring plasma concentrations. Each of these prerequisites will considered for the TKIs used to treat metastatic renal cancer (mRC).

3. Pharmacokinetics of TKIs and Their Variabilities

3.1. Pharmacokinetics

TKIs in general, and particularly the seven compounds used in mRC, all share similar PK pathways (Table 1). All present sufficient oral bioavailability to be taken orally, are mainly metabolized by CYP3A4, and are also substrates of ABC transporters such as ABCB1 (also called P-glycoprotein) and ABCG2 (BCRP). Their partition coefficient and, accordingly, their lipophilicity are high enough to allow for their passive diffusion through cellular membranes. They are highly bound to plasma proteins such as albumin and α1-glycoprotein acid. However, they present certain PK differences such as a lower oral bioavailability (≈20%) for pazopanib due to poor solubility within the digestive tract, and sunitinib and axitinib (both ≈50%) due to a substantial first-pass enteric effect greater than that of tivozanib (≈85%) [11]. Their metabolites are less active than the parent drug and/or are present at much lower plasma concentrations, except for N-desethyl sunitinib, which contributes to VEGFR inhibition [12]. Their mean half-life ranges between 2–5 h for axitinib to ≈100 h for cabozantinib and tivozanib.

3.2. Interindividual Pharmacokinetic Variability

The main PK parameter to consider is oral clearance (CL/F). CL corresponds to the elimination capacity of each patient to clear the drug from plasma, itself mainly dependent on hepatocyte CYP3A4 activity for TKIs; F corresponds to oral bioavailability, which may be much lower than 1 for some of these seven TKIs, and in certain circumstances for all (e.g., taking a gastrointestinal protecting agent too close to TKI administration). Indeed, individual plasma exposure between two drug administrations at steady-state (AUCτ,ss), the key parameter for PK/PD relationship (see following paragraph), is equal to the administered dose/(CL/F). As for the numerous drugs sharing these PK pathways (liver metabolism via CYP3A4 and efflux via P-gp), interindividual PK variability (IIV) is large, with coefficients of variation for both CL/F and AUC of 50–100% with extreme values of AUCτ,ss differing by a ratio of around 10. The factors involved in this variability are all well-known: liver function, drug–drug interaction (DDI), age, pharmacogenetics, etc. However, their respective impact cannot be foreseen, thus making predicting individual CL/F values impossible. In the end, the best (and only) way to phenotype each patient is to perform TDM [23].

3.3. Intrapatient Pharmacokinetic Variability

As expected for any drug, intrapatient PK variability of the seven TKIs is lower than their respective IIV. Moreover certain factors of intrapatient variability such as DDI, i.e., when a DDI perpetrator—a CYP inducer or inihibitor—is prescribed, represents one of the rationales for TDM. Nevertheless, a degree of intrapatient PK variability remains inexplicable and may be responsible for substantial interoccasion PK variability, thus hampering TDM. This is the case for pazopanib: interoccasion variability between 27 and 61% [16,24] has been observed, possibly questioning the representativeness of the observed concentration with respect to actual global exposure.

4. Pharmacokinetic–Pharmacodynamic Relationships for TKI in Treating Renal Cancer

4.1. Limits of Standard Doses

Standard doses of TKIs including those used in mRC are associated with widespread pharmacodynamic effects on both efficacy and adverse events. In terms of efficacy, drug switching is applied if the treatment is no longer effective. Recommendations are made by the manufacturing firm or proposed by investigators for each drug in the case of adverse events. For example, for cabozantinib in case of grade 3 or unacceptable grade 2 adverse events with the standard dose of 60 mg/day, it is recommended to discontinue the drug until adverse events become less severe than grade 1, then to resume treatment at a decreased dose (i.e., 40 mg/day, then 20 mg/day in the case of a second adverse event) [25]. For axitinib, a detailed algorithm has been proposed describing the recommended dose as a function of observed hypertension or proteinuria [26]. This need for individual dosing thus confirms the fact that the PD effects of TKIs are dose-dependent and call for discussion of the methodology used to choose their standard dosage. Phase 1 clinical trials of oral targeted therapies are performed similarly to those traditionally applied to conventional cytotoxic anticancer drugs. A limited number of patients are included at several successively increasing dosage levels up to the maximum tolerated dose. However, since the toxic profile of targeted therapies differs from that of cytotoxic chemotherapies with more chronic adverse events, it has been recommended to extend the period of studying toxicity for TKIs usually to two cycles (each one corresponding to a 3-week period) vs. a single cycle for cytotoxics [27]. Moreover, the number of patients treated at the recommended dose for phase 2 (RP2D) trials is usually larger for targeted therapies (at least 12 patients rather than six for cytotoxics). Despite these customizations, a substantial proportion of patients (around 50%) included in phase 3 trials, and later in routine practice, require dose modifications due to drug-related toxicity [28]. Furthermore, these observations are limited to side effects only. When considering PK-PD relationships (detailed below), a proportion of patients who do, in fact, tolerate well the standard dose are under-exposed and could benefit from higher dosage [29].

4.2. Pharmacokinetic–Pharmacodynamic Relationships

As explained above, one of the prerequisites for TDM implementation is confirming the existence of a PK/PD relationship with regards to efficacy and/or safety. The most robust clinical endpoints for exposure–response analysis are dose-limiting toxicity (DLT) or toxicity grade ≥ 3 onset for safety; and for efficacy, progression-free survival (PFS) and overall survival (OS). Various PK/PD relationships use different steady-state PK biomarkers such AUCτ,ss, peak concentration (Cmax,ss) and trough concentration (Cmin,ss). Cmax,ss corresponds to the highest drug concentration in the plasma, while Cmin,ss is the drug concentration just before the next administered dose. Table 2 summarizes the different PK/PD relationships for axitinib, cabozantinib, pazopanib, sorafenib and sunitinib. As far as we know, no exposure–efficacy data are currently available for lenvatinib or tivozanib in mRC, but reports of drug development show a trend in exploring the PK/toxicity relationship.
The pharmacodynamic effects of pazopanib, axitinib, cabozantinib, and sorafenib are mainly driven by the parent drug, which explains why only plasma exposure to the parent drug has been used as a PK biomarker. In contrast, preclinical experiments have shown that N-desmethyl sunitinib (SU12662) is an active metabolite [12]. Furthermore, its plasma exposure represents around a third of that of the parent drug in humans, suggesting that SU12662 significantly contributes to the PD effect of sunitinib malate. In this context, plasma concentrations of both sunitinib and SU12662 have been considered in PK/PD studies. The main side effect corresponding to a class-effect of VEGFR-TKIs, i.e., hypertension, is frequently correlated with plasma TKI exposure. Whatever the TKI, higher plasma exposure clearly contributes to the onset of DLT or toxicity grade ≥ 3. Furthermore, different threshold values of PK biomarkers (AUCτ,ss, Cmin,ss, or Cmax,ss) have been proposed to prevent DLT onset [30,31,32,33,34]. These threshold values could help physicians document whether DTL onset is concentration-dependent (i.e., due to TKI plasma overexposure). In this case, decreasing TKI dosing without compromising the antitumor effect could be proposed. As for efficacy, the PK/PD relationship is less self-evident since the antitumor response is multifactorial. For pazopanib, the target Cmin,ss > 20 μg/mL from PK/PD data of a phase 2 trial [35] has been confirmed in different studies [32,36,37] and therefore can be used in daily clinical practice. PK/PD data from the phase 3 METEOR trial 20 suggest a target for cabozantinib of Cmin,ss ≥ 750 ng/mL, but other «real-world» studies [32,38,39] have not confirmed this. Furthermore, these studies also showed that patients with Cmin,ss ≥ 750 ng/mL would be more at risk of developing DLT. Therefore, other studies are warranted to refine target Cmin,ss. For sunitinib, the target Cmin,ss > 50 ng/mL has been proposed using PK/PD data from preclinical and phase 1 studies [14,40]. Current data from PK/PD studies in mRC patients do not make it possible to draw any conclusion about the robustness of this target Cmin,ss in this population. Finally, the axitinib target AUCτ,ss ≥ 300 ng/mL∙h from PK/PD data of a phase 2 trial [41] has been confirmed in a single study [42], while no target value can be proposed for Cmin,ss and Cmax,ss [33,34]. Overall, it is possible to note that all of these PK-PD relationships were expected, since both efficacy and adverse events are tightly linked with competitive ATP inhibition of TK receptors, particularly of VEGFR. TDM could be useful in daily clinical practice even if the robustness of target concentrations remains questionable for two reasons. First, PK/PD data from phase 2/3 studies include selected patients with a better general health status than that observed in «real-world» patients. Second, the statistical power in several observational studies remains difficult to confirm, and the therapeutic range problematic to refine due to the small sample sizes of these studies.
Table
Table 2. Pharmacokinetic/pharmacodynamic relationships of the seven tyrosine kinase inhibitors prescribed in metastatic renal cancer.
Table 2. Pharmacokinetic/pharmacodynamic relationships of the seven tyrosine kinase inhibitors prescribed in metastatic renal cancer.
Number of
Patients		PK MarkerPD EfficacyPD TolerabilityRef.
Sorafenib52Cmin,ss Threshold Cmin,ss for onset of grade ≥ 2 hand–foot skin reaction and hypertension: 5.78 and 4.78 μg/mL, respectively[43]
Sunitinib149Composite AUCss (sunitinib+SU12662)Positive relationship between composite AUCss probability of response (partial of complete) or stable disease
Association between higher composite AUCss and longer TTP and OS 		Negative relationship between cumulated composite AUCss at day 28 and absolute neutrophil count[12]
55AUCss
Composite AUCss (sunitinib+SU12662)		Longer median OS in patients with composite AUCss > 1973 ng/mL∙h: 35.2 (CI 95%, 26.5–ND) vs. 16.7 (CI 95%, 4.3–ND) months; p = 0.0051
Trend for longer median PFS in patients with composite AUCss > 1973 ng/mL∙h: 35.2 (CI 95%, 8.0–ND) vs. 8.4 (CI 95%, 3.7–ND) months; p = 0.15		High sunitinib AUCss: independent risk factor of grade ≥ 3 acute toxicity (OR = 1.16 (CI 95%, 1.05–1.28); p = 0.005)
High SU12662 AUCss: independent risk factor of grade ≥ 2 thrombocytopenia (OR = 1.27 (CI 95%, 1.03–1.57); p = 0.028)		[44]
21 Composite Cmin,ss (sunitinib+SU12662)Patients with composite Cmin,ss < 100 ng/mL vs. those with composite Cmin,SS >100 ng/mL:
Longer median TTF: 590 vs. 71 days, respectively; p = 0.04
Longer median PFS: 748 vs. 238 days, respectively; p = 0.02
Trend for longer median: 939 vs. 570 days; p = 0.07		Positive relationship between increased composite Cmin,ss and severity of anorexia (p < 0.05) and fatigue (p < 0.05)
Inverse correlation between composite Cmin,ss and platelet counts (p < 0.05)
Higher incidence of grade ≥ 3 toxicities in patients with composite Cmin,ss > 100 ng/mL (75.0% vs. 23.1%, respectively)		[30]
20 Composite Cmin,ss (sunitinib+SU12662)Patients with composite Cmin,ss < 50 ng/mL vs. those with Cmin,ss > 50 ng/mL:
Longer median TTF: 743 (CI 95%, 217–1583) vs. 56 (CI 95%, 21–179) days; p < 0.001
Longer median PFS: 731 (CI 95%, 197–1576) vs. 95 (CI 95%, 197–1576) days; p < 0.001		Higher median composite Cmin,ss within 6 weeks in patients with DLT than in those without: 92.7 (range 52.7–196.9) vs. 43.4 (38.3–54.1) ng/mL; p = 0.001[45]
63Composite AUCss (sunitinib+SU12662)At disease progression, trend to a lower composite AUCss than the one during the first cycle: 1678 vs. 2004 ng/mL.h, respectively; p = 0.072
Median PFS not statistically longer in patients with composite AUCss > 2150 ng/mL.h at cycle 1: 14.8 (CI 95%, 2.7–26.9) vs. 11.4 (CI 95%, 5.8–17.0) months; p = 0.45 		High composite AUCss: independent risk factor of grade ≥ 3 acute toxicity (OR = 2.72 (CI 95%, 1.84–4.02); p < 0.0001)
→ Target composite AUCss < 2150 ng/mL.h to prevent onset of grade ≥ 3 acute toxicity 		[46]
Cabozantinib319 Cmin,SS Average Cmin,ss: 375, 750 and 1125 ng/mL for 20-, 40-, and 60-mg day, respectively.
HR for risk of progressive disease or death:
-
40 vs. 60 mg/day: 1.10 (CI 95%, 1.07–1.12)
-
20 vs. 60 mg/day: 1.39 (CI 95%, 1.29–1.49)
→ Target Cmin,SS > 750 ng/mL for efficacy		Association between an increase in average Cmin,ss and increased risk:
-
palma–plantar erythrodysesthesia syndrome (grade ≥ 1)
-
fatigue/asthenia (grade ≥ 3)
-
hypertension (systolic blood pressure > 160 mmHg or diastolic blood pressure > 100 mmHg)
-
diarrhea (grade ≥ 3)
[20]
76 Cmin,ss
AUCss
Cmax,ss 		Lower median exposure in patients with progressive disease vs. patients with best disease control
-
Cmin,ss: 406 vs. 634 ng/mL; p = 0.001
-
AUCss: 16 vs. 20 mg/mL.h; p = 0.037
Target Cmin,ss > 537 ng/mL for efficacy 		Higher median exposure in patients with DLT than in those without
-
Cmax,ss: 732 vs. 531 ng/mL; p = 0.006
-
AUCss: 21 vs. 16 mg/mL.h; p = 0.046
Target Cmin,ss < 618 ng/mL to prevent onset of DLT		[31]
25 Cmin,SSNo difference in PFS in patients with Cmin,SS < 573 ng/mL with others: 19.0 (CI 95%, 0–45.7) vs. 34 (CI 95%, 32.6–35.5) weeks, respectively Trend for higher median Cmin,ss in patients with DLT than in those without: 769 ng/mL (CI 95%, 663–893) vs. 568 ng/mL (CI 95%, 384–842), respectively; p = 0.079[39]
59Cmin,ssNo statistical difference in PFS in patients with average Cmin,SS ≥ 750 ng/mL (over the whole treatment period) compared to others: 19 (CI 95%, 0–40) vs. 52 (CI 95%, 34–70) weeks, respectively; p = 0.2
Trend for longer PFS in patients with average Cmin,SS ≤ 572 ng/mL (over the whole treatment period) compared to others: 65 (CI 95%, not reached) vs. 42 (CI 95%, 20–64) weeks, respectively; p = 0.055
No statistical difference in OS in patients with average Cmin,SS ≤ 572 ng/mL (over the whole treatment period) compared to others		Higher median Cmin,ss in patients with DLT than in those without: 831 (CI 95%, 711–1040) vs. 569 ng/mL (CI 95%, 494–754); p = 0.001
Higher DLT incidence in patients with a Cmin,ss ≥ 750 ng/mL at start dose compared to patients with an exposure < 750 ng/mL (78.6% vs. 38.7%; p = 0.003)		[38]
Pazopanib177Cmin,ssLonger median PFS in patients with Cmin,ss > 20.5 μg/mL at week 4:
52.0 vs. 19.6 weeks, respectively; p = 0.00378
Five-fold greater median observed tumour shrinkage in patients with Cmin,ss > 20.5 μg/mL at week 4: 37.9% vs. 6.9%.		Increased incidence of grade ≥ 3 toxicity in patients with Cmin,ss in the fourth quartile (36 to 85 μg/mL) [35]
35Cmin,ssLonger median PFS in patients with Cmin,ss > 20 μg/mL: 34.1 vs. 12.5 weeks, respectively; p = 0.0271;
Cmin,SS > 20 μg/mL: independent protective factor for death, HR 0.25 (CI 95%, 0.076–0.81); p = 0.021		 [36]
311Cmin,ssPatients achieving early or late Cmin,ss > 20.5 μg/mL had significantly longer disease-free survival:
-
Group early Cmin,SS: not estimable vs. 29.5 months, HR 0.556 (95% CI, 0.337-0.918); p = 0.0055
Group late Cmin,ss: not estimable vs. 29.9 months, HR 0.583 95% CI, 0.369-0.921); p = 0.0078		Increased incidence of grade ≥ 3 hypertension according to quartile in group early Cmin,ss[37]
27 Cmin,SSObjective response rate (complete response or partial response) similar in patients with Cmin,SS between 20.5 to 50.3 μg/mL and those with Cmin,SS ≥ 50.3 μg/mL (45.5 vs. 46.2%)
No objective response observed in patients with Cmin,ss < 20.5 μg/mL		Positive relationship between increased Cmin,ss and severity (grade 0–1 vs. grade ≥ 2) of anorexia (p < 0.05), fatigue (p < 0.05) and hypertension (p < 0.05)
Higher incidence of grade ≥ 3 toxicity in patients with a Cmin,ss ≥ 50.3 μg/mL (61.5% vs. 7.1%)
→ Target Cmin,ss< 50.3 μg/mL to prevent onset of grade ≥ 3 toxicities 		[32]
Axitinib168AUCssLonger median PFS in patients with AUCss ≥ 300 ng/mL∙h: 13.8 vs. 7.4 months; HR 0.558 (95% CI, 0.379–0.823); p = 0.003
Longer median OS in patients with AUCss ≥ 300 ng/mL∙h: 37.4 vs. 15.8 months; HR 0.489 (95% CI, 0.324–0.738); p < 0.001		Weak correlation between AUCss and blood diastolic pressure (r2 < 0.10) [41]
33Cmin,ss
AUCss		Longer OS in patients with Cmin,ss ≥ 5 ng/mL: median not reached vs. 299 days, respectively; p = 0.022)
Longer OS in patients with AUCss ≥ 300 ng/mL∙h: median not reached vs. 409 days, respectively; p = 0.045)
No relationship between PFS and AUCss or Cmin,ss		Threshold value Cmin,ss: 6.6 and 7.1 ng/mL to predict grade ≥ 2 hypothyroidism (p = 0.005)
and grade ≥ 2 anorexia (p = 0.035), respectively		[42]
20Cmax,ss Higher Cmax,ss higher in responders (complete or partial response) than in non-responders (stable or progression disease); p = 0.013
Longer median PFS in patients with Cmax,ss > 12.4 ng/mL: 799 (95% CI, 140–not estimable) vs. 336 days (95% CI, 70–not estimable) days; p = 0.047		Higher cumulative incidence of DLT in patients with Cmax,ss ≥ 40.2 ng/mL: sub-hazard ratio, 4.13 (95% CI, 1.27–13.5); p = 0.019[33]
35Cmin,ss,first
(2 weeks after treatment start)
Cmin,ss,1-3m (mean Cmin,ss between 1 and 3 months after treatment start)		Statistical association between best response and plasma exposure (Cmin,ss,first and Cmin,ss,1-3m)
Higher plasma exposure in patients with PFS ≥ 5 months:
-
Cmin,ss,first: 1.24 vs. 0.52 ng/mL, respectively; p = 0.003
-
Cmin,ss,1-3m: 1.76 vs. 0.57 ng/mL, respectively; p = 0.001
Trend to higher Cmin,ss,1-3m in patients with OS ≥ 25 months: 1.64 vs. 0.64 ng/mL, respectively; p = 0.097		Higher plasma exposure in patients with grade ≥ 3 toxicity:
-
Cmin,ss,first: 3.17 vs. 0.73 ng/mL, respectively; p = 0.012
-
Cmin,ss,1-3m: 2.50 vs. 0.73 ng/mL, respectively; p = 0.003
[34]
Tivozanib432Caverage over a 4 weeks treatment period Logistic regression between Caverage and probability for hand–foot syndrome: OR = 1.2 [95%CI: 1.00–1.03][47]
Lenvatinib260Cmin,ss Plasma exposure based on starting dose was a significant predictor for the occurrence of any grade proteinuria, nausea, and vomiting, and for Grade 3 or higher hypertension[19]
Cmin,ss, trough concentration at steady-state; Cmax,ss, peak concentration at steady-state; Caverage, mean concentration; AUCss, area-under-the-curve between two administrations at steady-state; TTP, time to progression; OS, overall survival; OR, overall response; PFS, progression free survival; ND, not determined; HR, hazard ratio; OR, odds ration; CI, confidence interval.

5. Benefit of TDM in Clinical Practice

5.1. TDM as a Predictive Factor for Clinical Outcome

Different studies using TDM for dosing adjustments (e.g., for pazopanib, sunitinib) have been conducted in patients with mRC [48,49] or solid tumors [29,50,51]. Herein lies the real benefit of TDM: in the case of observed Cmin,ss below threshold efficacy in a patient with “too” good tolerance, TDM allows the physician to increase the individual dose to reach optimal exposure. On the contrary (i.e., Cmin,ss higher than the threshold value together with observation of poor tolerance), TDM results provide other information in addition to clinical observations, allowing the physician to adjust the dosage less empirically. For example, a recent study reported that 14 out of 31 mRC patients (45%) presented a sunitinib Cmin,ss within the 50–100 ng/mL range [49]. In other patients, Cmin,ss was below 50 ng/mL in two cases (7%) and > 100 ng/mL in 15 (48%). For patients under- or over-exposed, PK model-based recommendations for tailored dosing ranged from 12.5 to 100 mg, i.e., a −75% to +100% change as compared with initial standard dosing. However, PK model-based recommendations being retrospective, the authors were not able to assess the clinical benefit of TDM. Moreover, the low number of patients in this study limited the scope. Verheijen et al. showed the feasibility of a PK-guided approach for pazopanib (target Cmin,ss > 20 μg/mL) in 30 mRC patients with repeated blood sampling [48]. Over the treatment course, 17 patients (57%) had at least one Cmin,ss < 20 μg/mL following the standard daily dose of 800 mg, and 10 of them were successfully treated with a PK-guided dose escalation, leading to daily dosing ranging from 1000 to 1800 mg. These results are particularly noteworthy since the intrapatient PK variability of pazopanib is higher than that of the other TKIs, allowing us to speculate that the possibility of reaching a target concentration would be even greater for the other VEGFR-TKIs.
The recent Dutch Pharmacology Oncology Group Therapeutic Drug Monitoring study (DPOG-TDM) has assessed the feasibility and the benefit of PK-guided dose optimization in 552 cancer patients for those patients underexposed [29]. In evaluating patients treated with sunitinib (n = 50) and pazopanib (n = 49), PK-guided dose interventions were successful in 80% of cases. However, it should be noted that “success” was based only on pharmacokinetic and toxicity criteria (i.e., if the median Cmin,ss after intervention was above the predefined TDM target concentrations, and if no dose reduction due to toxicity was needed within one month). Due to its non-randomized design, it was not possible to compare efficacy corresponding to TDM intervention with standard treatment. Splitting intake moments is highly recommended to increase drug bioavailability in patients underexposed at the approved dose of pazopanib (800 mg/day). This strategy is related to the saturation of intestinal absorption above an 800 mg-dose [52]. Groenland et al. showed that administering 400 mg twice daily resulted in a 79% increase in Cmin,ss compared to 800 mg once daily, with acceptable safety [53]. For sorafenib, PK-guided dose interventions were rarely feasible due to toxicity. Nevertheless, it is also important to note that the splitting strategy can be used for sorafenib doses above 400 mg twice daily due to saturation of intestinal absorption [54]. Finally, the benefit of TDM for axitinib could not be assessed since only two patients were included. The main limitation of published TDM studies in mRC patients is the lack of a control group to clearly evaluate the benefit of the PK-guided approach. To this end, a large multicenter randomized controlled trial with PFS or overall survival as an endpoint would be warranted, such as that previously conducted for imatinib in chronic myelogenous leukemia patients [55]. However, the rapid turnover of TKIs, added to their future combination with immunotherapy, prevents pharmacologists from designing and conducting such a trial.

5.2. Patients with Poor Adherence

The adherence to oral targeted therapies for cancer has not been formally studied in patients with mRC. However, several studies performed with TKIs in other diseases such as chronic myeloid leukemia showed that an adherence rate below 90% can be seen in nearly 25% of patients [56]. One of the objectives of TDM is to identify poorly adherent patients by observing a low TKI plasma level. However, while plasma concentrations below or just above the quantification limit of the assay may reveal poor adherence, some low concentrations may result from either poor adherence or a high metabolic capacity with no possibility of discriminating between these two situations. This is a key fact since the appropriate recommendation in each case is not the same: for poor adherence, therapeutic education would improve treatment adherence and no dose change would be recommended; for high oral CL, on the contrary, a higher daily dose of TKI should be used. The hypothesis can be raised that simultaneously determining the concentrations of the drug (parent compound) and one (or more) of its metabolites would make it possible to differentiate between these two patient profiles since patients with high metabolic capacity should have relatively high metabolite concentrations, while poorly adherent patients would have very low metabolite concentrations. This hypothesis has never been demonstrated or even evaluated for TKIs, but has been shown for atorvastatin; monitoring the drug and its two metabolites was more successful than monitoring atorvastatin concentrations alone in identifying non-adherent patients [57].

5.3. Management of Drug–Drug Interactions

Patients with mRC are usually treated with a large number of co-medications, thereby increasing the risk of DDI [58]. In particular, calcium-channel blockers are often given as antihypertensive drugs to treat this TKI-induced condition [59]. Since some of them are weak or moderate CYP3A inhibitors, clinicians must choose between changing the antihypertensive at the risk of unbalancing blood pressure, or allowing the DDI at the risk of increased toxicity. In the case of well-controlled blood pressure, TDM makes it possible to maintain the antihypertensive drug and adapt the TKI dose only in the case of significant overexposure.

6. Clinical Practices and Practical Issues

6.1. AUCτ,ss vs. Cmin,ss as the Best Marker of Systemic Exposure

Several observations corresponding to PK data obtained during the drug’s clinical development confirm that the best marker of drug plasma exposure is AUCτ,ss corresponding to the area-under-the-curve of plasma vs. time concentrations between two administrations at steady-state. This determination requires several blood samplings compatible with a clinical trial methodology but not with routine practice, particularly in an outpatient context. Fortunately, however, close correlations have also been reported between AUCτ,ss and Cmin,ss with regards to TKI administration schedule that were indeed to be expected; daily administration is associated with limited fluctuations of plasma concentrations, given their relatively long elimination half-life. Furthermore, individual Cmin,ss values are similar to the corresponding individual Cmean,ss values that are equal to AUCτ,ss/τ. For all of these reasons, the concept of a Cmin,ss target value, rather than a target AUCτ,ss, can be applied to TDM of TKIs in mRC.

6.2. Estimation of Cmin,ss from a Blood Sample Collected during the Dosing Interval

On many occasions for practical purposes (e.g., outpatient and medical appointments differing from normal daily drug intake time), the actual Cmin,ss cannot be determined. The measured level may be compared directly to the target Cmin,ss only for TKIs with a particularly long half-life such as cabozantinib (around 55 h). For other TKIs, and even for cabozantinib, if the blood sample is obtained around Cmax (shortly after drug intake), estimation of Cmin,ss from the observed C would allow for a more accurate interpretation of TDM. This estimation may be performed by an a posteriori Bayesian approach implemented in several software tools. This consists in using a population PK model previously published for a drug to estimate the most likely individual PK parameters for the patient given her/his observed C. Figure 1 displays the result of a Bayesian analysis of pazopanib plasma concentration observed during the dosing interval to estimate Cmin,ss.

6.3. Pre-Analytical Aspects and Assay

Most TKIs, including the seven compounds used in mRC, are stable in blood at room temperature in the presence of light. Moreover, centrifugation of blood samples to obtain plasma can be delayed up to 72 h, facilitating sample shipment questions and delayed analysis. The HPLC or UPLC method coupled with MS-MS detection are generally used to quantify plasma TKI levels. The chromatographic step of the analysis is preceded by extracting the compounds from the plasma, usually performed by organic precipitation using either methanol or acetonitrile. Total (i.e., free and plasma bound) drug concentrations are thus measured.

6.4. Interpretation of TDM Results

The main result of TDM is the observed concentration value together, if necessary, with the estimated Cmin,ss value. However, it is also necessary to provide bibliographic information allowing the physician to analyze this observed value together with clinical observation of the patient. As presented above, there is no universal threshold to classify exposure values in a toxic or efficacious zone. Therefore, the results should be interpreted in terms of over- or under-exposure compared to a median value or a range of observed values.
Below are the commentaries we propose for each of the seven TKIs.
Cabozantinib: the observed median residual concentration = 500 ng/mL (365.5–742.5) in a real-life study of 76 patients [31]; suggested that the efficiency threshold in this study is >530 ng/mL and the suggested toxicity threshold in this study is >620 ng/mL, to be compared with clinical tolerance.
Pazopanib: a residual concentration > 20.6 mg/L is necessary to obtain optimal efficiency [35].
Sorafenib: a residual concentration > 4.78 mg/L is associated with the development of major adverse effects [43]. Even if there is no validated efficiency threshold, we recommend a Cmin concentration between 3.75 and 4.30 mg/L, the mean residual concentration observed in different studies being 4.2 mg/L [61]
Sunitinib: a total residual concentration (sunitinib + its active metabolite SU 12662) greater than 50 µg/L is required for optimum efficiency. This total residual concentration must be less than 100 µg/L to avoid the risk of toxicity [40]. During a daily schedule with no therapeutic window (37.5 mg/day continuously), this total concentration must be between 37.5 µg/L and 100 µg/L for optimum efficiency and to avoid potentially increased toxicity [61].
Regardless of the result, PK variability factors should be considered to interpret exposure values before proposing a dose adjustment. In the case of supratherapeutic exposure, different PK variability factors should be considered: CRP level [62], low albumin level, sarcopenia [63], and PK drug–drug interactions (CYP and P-gp inhibitors 11, complementary and alternative medicines). Finally, the influence of food on PK should not be underestimated for drugs such as cabozantinib and pazopanib [64]. With no explanations from previous factors, some genetic polymorphisms such as CYP3A4*22 [65], U GT1A1/9 [66,67,68] and P-gp/BCRP [44,69] might need to be explored since they can significantly decrease metabolism and/or enhance TKI bioavailability. In the case of subtherapeutic exposure, different parameters should be explored: adherence, PK DDI (CYP inducer [11], complementary and alternative medicines) and proton pump inhibitor uptake for tivozanib and pazopanib [11,66].

6.5. Combinations of Tyrosine Kinase Inhibitors with Immunotherapy

The current trend is to include TKIs in combination with immunotherapy, either against PD-1 or PD-L1, in treating mRC. The combinations of pembrolizumab–axitinib, nivolumab–cabozantinib, and lenvatinib–pembrolizumab have become the most widely accepted first-line therapy. The greater efficacy of these combinations vs. TKI monochemotherapy has been demonstrated by several phase 3 clinical trials showing a synergistic, or at least an additive, antitumoral effect. Moreover, given the very different toxicity profile of CPIs and TKIs, combining these two drugs at their standard dosages as used in monotherapy may be an option to consider. Indeed, axitinib in combination with pembrolizumab is given at its standard dose (i.e., a starting dose of 5 mg twice daily with the possibility of increasing up to 7 mg twice daily in the case of good arterial pressure tolerance) [70]. However, based on the results of the phase 1 trial combining cabozantinib and nivolumab which showed that 60 mg/day (the standard dose in monotherapy) of cabozantinib led to a higher rate of adverse events despite the absence of DLT, the RP2D chosen was 40 mg/day of cabozantinib [71]. Thus far, no report of PK/PD studies corresponding to these combinations has been published. However, in parallel to the generalization of combination therapy, it will be important to re-evaluate the PK/PD relationship described for TKIs in monotherapy, particularly those between TKI plasma concentrations and adverse events. If these future results are superimposed over those observed in monotherapy, it will prove that their dose-limiting toxicity is independent of CPIs. Where the adverse events vs. plasma concentration curve shifts to the left, it would provide evidence that an overall lower dose and lower target concentrations should be used with these combinations.

6.6. Plasma Protein Binding

TKIs, and particularly the seven compounds used in renal cancer, are highly bound to plasma proteins, serum albumin and α1-acid glycoprotein, with an unbound fraction (fu) around or even less than 1% for most of them (Table 1). According to a general concept in clinical pharmacology, confirmed by specific observations of imatinib, PK/PD relationships are closer when unbound plasma concentrations (Cu) rather than total concentrations are considered, particularly when fu presents large IIV. Indeed, binding to plasma protein represents a limiting factor for TKI diffusion within cells. Assays used for TDM make it possible to determine (total) plasma concentrations. Thus, plasma protein binding should be kept in mind for patients with a low level of plasma protein and/or treated by another drug capable of displacing TKI from plasma proteins. In these patients, plasma concentrations within the target range may be associated with higher Cu, and consequently with a higher risk of adverse events.

6.7. Intrapatient Pharmacokinetic Variability

We have seen that one of the prerequisites for considering TDM for a drug is its limited intrapatient PK variability. Indeed, if such is not the case, a single observed plasma concentration may not be representative of drug exposure over a longer period. If intrapatient variability of each of the seven TKIs is indeed lower than their IIV, intrapatient PK is never negligible. Intrapatient variability has been estimated at 35% for pazopanib. Part of this variability is certainly linked to the food effect on pazopanib bioavailability. Simultaneous ingestion of food increases plasma exposure by approximately twofold, but to an extent this itself is dependent on lipid components. Finally, plasma exposure to pazopanib and sorafenib is known to decrease over the treatment course [52,72], especially after three months. This phenomenon can result in subtherapeutic exposure to TKI and insufficient anti-tumor effect. Using TDM would be particularly useful to restore therapeutic plasma exposure and therefore to optimize treatment time with pazopanib or sorafenib.

7. Conclusions

TDM represents a useful and complementary way to monitor patients with mRC treated by TKIs to individualize their dosage. There is a need for additional prospective clinical trials with PK-guided individualization, particularly within TKI–CPI combinations.

Author Contributions

F.P., M.M., L.M., D.P., B.B., E.C. and C.C. contributed to the writing and editing of this manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no funding.

Acknowledgments

We would like to thank Gail Taillefer, a native English speaker experienced in scientific publication, for her review of the manuscript.

Conflicts of Interest

The authors declare non conflict of interest.

References

  1. Michaelis, J.; Grabbert, M.; Sigle, A.; Yilmaz, M.; Schlager, D.; Gratzke, C.; Miernik, A.; Schoeb, D.S. Tyrosine Kinase Inhibitors in the Treatment of Metastasised Renal Cell Carcinoma-Future or the Past? Cancers 2022, 14, 3777. [Google Scholar] [CrossRef] [PubMed]
  2. Choueiri, T.K.; Powles, T.; Burotto, M.; Escudier, B.; Bourlon, M.T.; Zurawski, B.; Oyervides Juárez, V.M.; Hsieh, J.J.; Basso, U.; Shah, A.Y.; et al. Nivolumab plus Cabozantinib versus Sunitinib for Advanced Renal-Cell Carcinoma. N. Engl. J. Med. 2021, 384, 829–841. [Google Scholar] [CrossRef] [PubMed]
  3. Motzer, R.; Alekseev, B.; Rha, S.-Y.; Porta, C.; Eto, M.; Powles, T.; Grünwald, V.; Hutson, T.E.; Kopyltsov, E.; Méndez-Vidal, M.J.; et al. Lenvatinib plus Pembrolizumab or Everolimus for Advanced Renal Cell Carcinoma. N. Engl. J. Med. 2021, 384, 1289–1300. [Google Scholar] [CrossRef] [PubMed]
  4. Motzer, R.J.; Penkov, K.; Haanen, J.; Rini, B.; Albiges, L.; Campbell, M.T.; Venugopal, B.; Kollmannsberger, C.; Negrier, S.; Uemura, M.; et al. Avelumab plus Axitinib versus Sunitinib for Advanced Renal-Cell Carcinoma. N. Engl. J. Med. 2019, 380, 1103–1115. [Google Scholar] [CrossRef] [PubMed]
  5. Rini, B.I.; Plimack, E.R.; Stus, V.; Gafanov, R.; Hawkins, R.; Nosov, D.; Pouliot, F.; Alekseev, B.; Soulières, D.; Melichar, B.; et al. Pembrolizumab plus Axitinib versus Sunitinib for Advanced Renal-Cell Carcinoma. N. Engl. J. Med. 2019, 380, 1116–1127. [Google Scholar] [CrossRef]
  6. Apolo, A.B.; Powles, T.; Escudier, B.; Burotto, M.; Zhang, J.; Simsek, B.; Scheffold, C.; Motzer, R.J.; Choueiri, T.K. Nivolumab plus ipilimumab plus cabozantinib triplet combination for patients with previously untreated advanced renal cell carcinoma: Results from a discontinued arm of the phase III CheckMate 9ER trial. Eur. J. Cancer 2022, 177, 63–71. [Google Scholar] [CrossRef]
  7. Clarke, W.A.; Chatelut, E.; Fotoohi, A.K.; Larson, R.A.; Martin, J.H.; Mathijssen, R.H.J.; Salamone, S.J. Therapeutic drug monitoring in oncology: International Association of Therapeutic Drug Monitoring and Clinical Toxicology consensus guidelines for imatinib therapy. Eur. J. Cancer 2021, 157, 428–440. [Google Scholar] [CrossRef]
  8. Widmer, N.; Bardin, C.; Chatelut, E.; Paci, A.; Beijnen, J.; Levêque, D.; Veal, G.; Astier, A. Review of therapeutic drug monitoring of anticancer drugs part two--targeted therapies. Eur. J. Cancer 2014, 50, 2020–2036. [Google Scholar] [CrossRef]
  9. Chatelut, E.; Puisset, F. The scientific basis of body surface area-based dosing. Clin. Pharm. Ther. 2014, 95, 359–361. [Google Scholar] [CrossRef]
  10. Chatelut, E.; Hendrikx, J.J.M.A.; Martin, J.; Ciccolini, J.; Moes, D.J.A.R. Unraveling the complexity of therapeutic drug monitoring for monoclonal antibody therapies to individualize dose in oncology. Pharm. Res. Perspect. 2021, 9, e00757. [Google Scholar] [CrossRef]
  11. Fogli, S.; Porta, C.; Del Re, M.; Crucitta, S.; Gianfilippo, G.; Danesi, R.; Rini, B.I.; Schmidinger, M. Optimizing treatment of renal cell carcinoma with VEGFR-TKIs: A comparison of clinical pharmacology and drug-drug interactions of anti-angiogenic drugs. Cancer Treat Rev. 2020, 84, 101966. [Google Scholar] [CrossRef]
  12. Houk, B.E.; Bello, C.L.; Poland, B.; Rosen, L.S.; Demetri, G.D.; Motzer, R.J. Relationship between exposure to sunitinib and efficacy and tolerability endpoints in patients with cancer: Results of a pharmacokinetic/pharmacodynamic meta-analysis. Cancer Chemother. Pharm. 2010, 66, 357–371. [Google Scholar] [CrossRef]
  13. Jain, L.; Woo, S.; Gardner, E.R.; Dahut, W.L.; Kohn, E.C.; Kummar, S.; Mould, D.R.; Giaccone, G.; Yarchoan, R.; Venitz, J.; et al. Population pharmacokinetic analysis of sorafenib in patients with solid tumours. Br. J. Clin. Pharm. 2011, 72, 294–305. [Google Scholar] [CrossRef] [Green Version]
  14. Faivre, S.; Delbaldo, C.; Vera, K.; Robert, C.; Lozahic, S.; Lassau, N.; Bello, C.; Deprimo, S.; Brega, N.; Massimini, G.; et al. Safety, pharmacokinetic, and antitumor activity of SU11248, a novel oral multitarget tyrosine kinase inhibitor, in patients with cancer. J. Clin. Oncol. 2006, 24, 25–35. [Google Scholar] [CrossRef]
  15. Goodman, V.L.; Rock, E.P.; Dagher, R.; Ramchandani, R.P.; Abraham, S.; Gobburu, J.V.S.; Booth, B.P.; Verbois, S.L.; Morse, D.E.; Liang, C.Y.; et al. Approval summary: Sunitinib for the treatment of imatinib refractory or intolerant gastrointestinal stromal tumors and advanced renal cell carcinoma. Clin. Cancer Res. 2007, 13, 1367–1373. [Google Scholar]
  16. Ozbey, A.C.; Combarel, D.; Poinsignon, V.; Lovera, C.; Saada, E.; Mir, O.; Paci, A. Population Pharmacokinetic Analysis of Pazopanib in Patients and Determination of Target AUC. Pharmaceuticals 2021, 14, 927. [Google Scholar] [CrossRef]
  17. Hurwitz, H.I.; Dowlati, A.; Saini, S.; Savage, S.; Suttle, A.B.; Gibson, D.M.; Hodge, J.P.; Merkle, E.M.; Pandite, L. Phase I trial of pazopanib in patients with advanced cancer. Clin. Cancer Res. 2009, 15, 4220–4227. [Google Scholar] [CrossRef] [Green Version]
  18. FDA. C for DE and R: Axitinib Clinical Pharmacology and Biopharmaceutic Review [Internet]. 2012. Available online: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2012/202324orig1s000clinpharmr.pdf (accessed on 2 November 2022).
  19. EMA. C for MP for HU (CHMP): Lenvima-EPAR Public Assessment Report [Internet]. 2015. Available online: https://www.ema.europa.eu/en/documents/assessment-report/lenvima-epar-public-assessment-report_en.pdf (accessed on 2 November 2022).
  20. Lacy, S.; Nielsen, J.; Yang, B.; Miles, D.; Nguyen, L.; Hutmacher, M. Population exposure-response analysis of cabozantinib efficacy and safety endpoints in patients with renal cell carcinoma. Cancer Chemother. Pharm. 2018, 81, 1061–1070. [Google Scholar] [CrossRef] [Green Version]
  21. Singh, H.; Brave, M.; Beaver, J.A.; Cheng, J.; Tang, S.; Zahalka, E.; Palmby, T.R.; Venugopal, R.; Song, P.; Liu, Q.; et al. U.S. Food and Drug Administration Approval: Cabozantinib for the Treatment of Advanced Renal Cell Carcinoma. Clin. Cancer Res. 2017, 23, 330–335. [Google Scholar] [CrossRef] [Green Version]
  22. EMA. C for MP for HU (CHMP): Fotvida-EPAR Public Assessment Report [Internet]. 2017. Available online: https://www.ema.europa.eu/en/documents/assessment-report/fotivda-epar-public-assessment-report_en.pdf (accessed on 2 November 2022).
  23. Ornstein, M.C.; Rini, B.I. Pharmacokinetically Guided Dosing of Oral Drugs: True Precision Oncology? Clin. Cancer Res. 2016, 22, 5626–5628. [Google Scholar] [CrossRef] [Green Version]
  24. Imbs, D.-C.; Négrier, S.; Cassier, P.; Hollebecque, A.; Varga, A.; Blanc, E.; Lafont, T.; Escudier, B.; Soria, J.-C.; Pérol, D.; et al. Pharmacokinetics of pazopanib administered in combination with bevacizumab. Cancer Chemother. Pharm. 2014, 73, 1189–1196. [Google Scholar]
  25. FDA. Cabometyx Summary of Product Characteristics [Internet]. 2015. Available online: https://www.ema.europa.eu/en/documents/product-information/cabometyx-epar-product-information_en.pdf (accessed on 2 November 2022).
  26. Cohen, R.B.; Oudard, S. Antiangiogenic therapy for advanced renal cell carcinoma: Management of treatment-related toxicities. Investig. New Drugs 2012, 30, 2066–2079. [Google Scholar] [CrossRef] [PubMed]
  27. Postel-Vinay, S.; Collette, L.; Paoletti, X.; Rizzo, E.; Massard, C.; Olmos, D.; Fowst, C.; Levy, B.; Mancini, P.; Lacombe, D.; et al. Towards new methods for the determination of dose limiting toxicities and the assessment of the recommended dose for further studies of molecularly targeted agents--dose-Limiting Toxicity and Toxicity Assessment Recommendation Group for Early Trials of Targeted therapies, an European Organisation for Research and Treatment of Cancer-led study. Eur. J. Cancer 2014, 50, 2040–2049. [Google Scholar] [PubMed]
  28. Roda, D.; Jimenez, B.; Banerji, U. Are Doses and Schedules of Small-Molecule Targeted Anticancer Drugs Recommended by Phase I Studies Realistic? Clin. Cancer Res. 2016, 22, 2127–2132. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. Groenland, S.L.; van Eerden, R.A.G.; Westerdijk, K.; Meertens, M.; Koolen, S.L.W.; Moes, D.J.A.R.; de Vries, N.; Rosing, H.; Otten, H.; Vulink, A.J.E.; et al. Therapeutic drug monitoring-based precision dosing of oral targeted therapies in oncology: A prospective multicenter study. Ann. Oncol. 2022, 33, 1071–1082. [Google Scholar] [CrossRef]
  30. Noda, S.; Otsuji, T.; Baba, M.; Yoshida, T.; Kageyama, S.; Okamoto, K.; Okada, Y.; Kawauchi, A.; Onishi, H.; Hira, D.; et al. Assessment of Sunitinib-Induced Toxicities and Clinical Outcomes Based on Therapeutic Drug Monitoring of Sunitinib for Patients with Renal Cell Carcinoma. Clin. Genitourin. Cancer 2015, 13, 350–358. [Google Scholar] [CrossRef]
  31. Cerbone, L.; Combarel, D.; Geraud, A.; Auclin, E.; Foulon, S.; Alves Costa Silva, C.; Colomba, E.; Carril, L.; Derosa, L.; Flippot, R.; et al. Association of cabozantinib pharmacokinetics, progression and toxicity in metastatic renal cell carcinoma patients: Results from a pharmacokinetics/pharmacodynamics study. ESMO Open 2021, 6, 100312. [Google Scholar] [CrossRef]
  32. Noda, S.; Yoshida, T.; Hira, D.; Murai, R.; Tomita, K.; Tsuru, T.; Kageyama, S.; Kawauchi, A.; Ikeda, Y.; Morita, S.-Y.; et al. Exploratory Investigation of Target Pazopanib Concentration Range for Patients with Renal Cell Carcinoma. Clin. Genitourin. Cancer 2019, 17, e306–e313. [Google Scholar] [CrossRef]
  33. Fukudo, M.; Tamaki, G.; Azumi, M.; Kakizaki, H.; Matsumoto, S.; Tasaki, Y. Absorption of the orally active multikinase inhibitor axitinib as a therapeutic index to guide dose titration in metastatic renal cell carcinoma. Investig. New Drugs. 2021, 39, 595–604. [Google Scholar] [CrossRef]
  34. Synowiec, Z.; Sobańska, K.; Synowiec, T.; Teżyk, A.; Tomczak, P.; Jabłecka, A. Axitinib Trough Concentration and its Influence on the Efficacy and Toxicity of Second-line Renal Cell Carcinoma Treatment. Clin. Genitourin. Cancer 2022, 20, 390.e1–390.e8. [Google Scholar] [CrossRef]
  35. Suttle, A.B.; Ball, H.A.; Molimard, M.; Hutson, T.E.; Carpenter, C.; Rajagopalan, D.; Lin, Y.; Swann, S.; Amado, R.; Pandite, L. Relationships between pazopanib exposure and clinical safety and efficacy in patients with advanced renal cell carcinoma. Br. J. Cancer 2014, 111, 1909–1916. [Google Scholar] [CrossRef] [Green Version]
  36. Verheijen, R.B.; Swart, L.E.; Beijnen, J.H.; Schellens, J.H.M.; Huitema, A.D.R.; Steeghs, N. Exposure-survival analyses of pazopanib in renal cell carcinoma and soft tissue sarcoma patients: Opportunities for dose optimization. Cancer Chemother. Pharm. 2017, 80, 1171–1178. [Google Scholar]
  37. Sternberg, C.N.; Donskov, F.; Haas, N.B.; Doehn, C.; Russo, P.; Elmeliegy, M.; Baneyx, G.; Banerjee, H.; Aimone, P.; Motzer, R.J. Pazopanib Exposure Relationship with Clinical Efficacy and Safety in the Adjuvant Treatment of Advanced Renal Cell Carcinoma. Clin. Cancer Res. 2018, 24, 3005–3013. [Google Scholar] [CrossRef]
  38. Krens, S.D.; van Erp, N.P.; Groenland, S.L.; Moes, D.J.A.R.; Mulder, S.F.; Desar, I.M.E.; van der Hulle, T.; Steeghs, N.; van Herpen, C.M.L. Exposure-response analyses of cabozantinib in patients with metastatic renal cell cancer. BMC Cancer 2022, 22, 228. [Google Scholar]
  39. Krens, S.D.; van Boxtel, W.; Uijen, M.J.M.; Jansman, F.G.A.; Desar, I.M.E.; Mulder, S.F.; van Herpen, C.M.L.; van Erp, N.P. Exposure-toxicity relationship of cabozantinib in patients with renal cell cancer and salivary gland cancer. Int. J. Cancer 2022, 150, 308–316. [Google Scholar] [CrossRef]
  40. Mendel, D.B.; Laird, A.D.; Xin, X.; Louie, S.G.; Christensen, J.G.; Li, G.; Schreck, R.E.; Abrams, T.J.; Ngai, T.J.; Lee, L.B.; et al. In vivo antitumor activity of SU11248, a novel tyrosine kinase inhibitor targeting vascular endothelial growth factor and platelet-derived growth factor receptors: Determination of a pharmacokinetic/pharmacodynamic relationship. Clin. Cancer Res. 2003, 9, 327–337. [Google Scholar]
  41. Rini, B.I.; Garrett, M.; Poland, B.; Dutcher, J.P.; Rixe, O.; Wilding, G.; Stadler, W.M.; Pithavala, Y.K.; Kim, S.; Tarazi, J.; et al. Axitinib in metastatic renal cell carcinoma: Results of a pharmacokinetic and pharmacodynamic analysis. J. Clin. Pharm. 2013, 53, 491–504. [Google Scholar] [CrossRef] [Green Version]
  42. Igarashi, R.; Inoue, T.; Fujiyama, N.; Tsuchiya, N.; Numakura, K.; Kagaya, H.; Saito, M.; Narita, S.; Satoh, S.; Niioka, T.; et al. Contribution of UGT1A1 genetic polymorphisms related to axitinib pharmacokinetics to safety and efficacy in patients with renal cell carcinoma. Med. Oncol. 2018, 35, 51. [Google Scholar] [CrossRef]
  43. Fukudo, M.; Ito, T.; Mizuno, T.; Shinsako, K.; Hatano, E.; Uemoto, S.; Kamba, T.; Yamasaki, T.; Ogawa, O.; Seno, H.; et al. Exposure-toxicity relationship of sorafenib in Japanese patients with renal cell carcinoma and hepatocellular carcinoma. Clin Pharm. 2014, 53, 185–196. [Google Scholar] [CrossRef]
  44. Narjoz, C.; Cessot, A.; Thomas-Schoemann, A.; Golmard, J.L.; Huillard, O.; Boudou-Rouquette, P.; Behouche, A.; Taieb, F.; Durand, J.P.; Dauphin, A.; et al. Role of the lean body mass and of pharmacogenetic variants on the pharmacokinetics and pharmacodynamics of sunitinib in cancer patients. Investig. New Drugs 2015, 33, 257–268. [Google Scholar] [CrossRef]
  45. Takasaki, S.; Kawasaki, Y.; Kikuchi, M.; Tanaka, M.; Suzuka, M.; Noda, A.; Sato, Y.; Yamashita, S.; Mitsuzuka, K.; Saito, H.; et al. Relationships between sunitinib plasma concentration and clinical outcomes in Japanese patients with metastatic renal cell carcinoma. Int. J. Clin. Oncol. 2018, 23, 936–943. [Google Scholar] [CrossRef] [PubMed]
  46. Cabel, L.; Blanchet, B.; Thomas-Schoemann, A.; Huillard, O.; Bellesoeur, A.; Cessot, A.; Giroux, J.; Boudou-Rouquette, P.; Coriat, R.; Vidal, M.; et al. Drug monitoring of sunitinib in patients with advanced solid tumors: A monocentric observational French study. Fundam. Clin. Pharm. 2018, 32, 98–107. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. FDA. C for DE and R: Tivozanib Clinical and Pharmacology Review [Internet]. 2018. Available online: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2021/212904Orig1s000MultidisciplineR.pdf (accessed on 2 November 2022).
  48. Verheijen, R.B.; Bins, S.; Mathijssen, R.H.J.; Lolkema, M.P.; van Doorn, L.; Schellens, J.H.M.; Beijnen, J.H.; Langenberg, M.H.G.; Huitema, A.D.R.; Steeghs, N.; et al. Individualized Pazopanib Dosing: A Prospective Feasibility Study in Cancer Patients. Clin. Cancer Res. 2016, 22, 5738–5746. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  49. Ferrer, F.; Chauvin, J.; DeVictor, B.; Lacarelle, B.; Deville, J.-L.; Ciccolini, J. Clinical-Based vs. Model-Based Adaptive Dosing Strategy: Retrospective Comparison in Real-World mRCC Patients Treated with Sunitinib. Pharmaceuticals 2021, 14, 494. [Google Scholar] [CrossRef] [PubMed]
  50. Lankheet, N.A.G.; Kloth, J.S.L.; Gadellaa-van Hooijdonk, C.G.M.; Cirkel, G.A.; Mathijssen, R.H.J.; Lolkema, M.P.J.K.; Schellens, J.H.M.; Voest, E.E.; Sleijfer, S.; De Jonge, M.J.A.; et al. Pharmacokinetically guided sunitinib dosing: A feasibility study in patients with advanced solid tumours. Br. J. Cancer 2014, 110, 2441–2449. [Google Scholar] [CrossRef]
  51. Sabanathan, D.; Zhang, A.; Fox, P.; Coulter, S.; Gebski, V.; Balakrishnar, B.; Chan, M.; Liddle, C.; Gurney, H. Dose individualization of sunitinib in metastatic renal cell cancer: Toxicity-adjusted dose or therapeutic drug monitoring. Cancer Chemother. Pharm. 2017, 80, 385–393. [Google Scholar] [CrossRef]
  52. Yu, H.; van Erp, N.; Bins, S.; Mathijssen, R.H.J.; Schellens, J.H.M.; Beijnen, J.H.; Steeghs, N.; Huitema, A.D.R. Development of a Pharmacokinetic Model to Describe the Complex Pharmacokinetics of Pazopanib in Cancer Patients. Clin. Pharm. 2017, 56, 293–303. [Google Scholar] [CrossRef]
  53. Groenland, S.L.; van Eerden, R.A.G.; Verheijen, R.B.; de Vries, N.; Thijssen, B.; Rosing, H.; Beijnen, J.H.; Koolen, S.L.W.; Mathijssen, R.H.J.; Huitema, A.D.R.; et al. Cost-Neutral Optimization of Pazopanib Exposure by Splitting Intake Moments: A Prospective Pharmacokinetic Study in Cancer Patients. Clin. Pharm. 2020, 59, 941–948. [Google Scholar] [CrossRef]
  54. Hornecker, M.; Blanchet, B.; Billemont, B.; Sassi, H.; Ropert, S.; Taieb, F.; Mir, O.; Abbas, H.; Harcouet, L.; Coriat, R.; et al. Saturable absorption of sorafenib in patients with solid tumors: A population model. Investig. New Drugs 2012, 30, 1991–2000. [Google Scholar] [CrossRef]
  55. Gotta, V.; Widmer, N.; Decosterd, L.A.; Chalandon, Y.; Heim, D.; Gregor, M.; Benz, R.; Leoncini-Franscini, L.; Baerlocher, G.M.; Duchosal, M.A.; et al. Clinical usefulness of therapeutic concentration monitoring for imatinib dosage individualization: Results from a randomized controlled trial. Cancer Chemother. Pharm. 2014, 74, 1307–1319. [Google Scholar] [CrossRef]
  56. Marin, D.; Bazeos, A.; Mahon, F.-X.; Eliasson, L.; Milojkovic, D.; Bua, M.; Apperley, J.F.; Szydlo, R.; Desai, R.; Kozlowski, K.; et al. Adherence is the critical factor for achieving molecular responses in patients with chronic myeloid leukemia who achieve complete cytogenetic responses on imatinib. J. Clin. Oncol. 2010, 28, 2381–2388. [Google Scholar] [CrossRef]
  57. Karvaly, G.B.; Karádi, I.; Vincze, I.; Neely, M.N.; Trojnár, E.; Prohászka, Z.; Imreh, É.; Vásárhelyi, B.; Zsáry, A. A pharmacokinetics-based approach to the monitoring of patient adherence to atorvastatin therapy. Pharm. Res. Perspect. 2021, 9, e00856. [Google Scholar] [CrossRef]
  58. Azam, C.; Claraz, P.; Chevreau, C.; Vinson, C.; Cottura, E.; Mourey, L.; Pouessel, D.; Guibaud, S.; Pollet, O.; Le Goff, M.; et al. Association between clinically relevant toxicities of pazopanib and sunitinib and the use of weak CYP3A4 and P-gp inhibitors. Eur. J. Clin. Pharm. 2020, 76, 579–587. [Google Scholar] [CrossRef]
  59. Agarwal, M.; Thareja, N.; Benjamin, M.; Akhondi, A.; Mitchell, G.D. Tyrosine Kinase Inhibitor-Induced Hypertension. Curr. Oncol. Rep. 2018, 20, 65. [Google Scholar] [CrossRef]
  60. Le Louedec, F.; Puisset, F.; Thomas, F.; Chatelut, É.; White-Koning, M. Easy and reliable maximum a posteriori Bayesian estimation of pharmacokinetic parameters with the open-source R package mapbayr. CPT Pharmacomet. Syst. Pharm. 2021, 10, 1208–1220. [Google Scholar] [CrossRef]
  61. Yu, H.; Steeghs, N.; Nijenhuis, C.M.; Schellens, J.H.M.; Beijnen, J.H.; Huitema, A.D.R. Practical guidelines for therapeutic drug monitoring of anticancer tyrosine kinase inhibitors: Focus on the pharmacokinetic targets. Clin. Pharm. 2014, 53, 305–325. [Google Scholar] [CrossRef]
  62. Simon, F.; Gautier-Veyret, E.; Truffot, A.; Chenel, M.; Payen, L.; Stanke-Labesque, F.; Tod, M. Modeling Approach to Predict the Impact of Inflammation on the Pharmacokinetics of CYP2C19 and CYP3A4 Substrates. Pharm. Res. 2021, 38, 415–428. [Google Scholar] [CrossRef]
  63. Mir, O.; Coriat, R.; Blanchet, B.; Durand, J.-P.; Boudou-Rouquette, P.; Michels, J.; Ropert, S.; Vidal, M.; Pol, S.; Chaussade, S.; et al. Sarcopenia predicts early dose-limiting toxicities and pharmacokinetics of sorafenib in patients with hepatocellular carcinoma. PLoS ONE 2012, 7, e37563. [Google Scholar] [CrossRef]
  64. Veerman, G.D.M.; Hussaarts, K.G.A.M.; Jansman, F.G.A.; Koolen, S.W.L.; van Leeuwen, R.W.F.; Mathijssen, R.H.J. Clinical implications of food-drug interactions with small-molecule kinase inhibitors. Lancet Oncol. 2020, 21, e265–e279. [Google Scholar] [CrossRef]
  65. Bins, S.; Huitema, A.D.R.; Laven, P.; Bouazzaoui, S.E.; Yu, H.; van Erp, N.; van Herpen, C.; Hamberg, P.; Gelderblom, H.; Steeghs, N.; et al. Impact of CYP3A4*22 on Pazopanib Pharmacokinetics in Cancer Patients. Clin. Pharm. 2019, 58, 651–658. [Google Scholar] [CrossRef] [Green Version]
  66. Mir, O.; Touati, N.; Lia, M.; Litière, S.; Le Cesne, A.; Sleijfer, S.; Blay, J.-Y.; Leahy, M.; Young, R.; Mathijssen, R.H.J.; et al. Impact of Concomitant Administration of Gastric Acid-Suppressive Agents and Pazopanib on Outcomes in Soft-Tissue Sarcoma Patients Treated within the EORTC 62043/62072 Trials. Clin. Cancer Res. 2019, 25, 1479–1485. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  67. Peer, C.J.; Sissung, T.M.; Kim, A.; Jain, L.; Woo, S.; Gardner, E.R.; Kirkland, C.T.; Troutman, S.M.; English, B.C.; Richardson, E.D.; et al. Sorafenib is an inhibitor of UGT1A1 but is metabolized by UGT1A9, implications of genetic variants on pharmacokinetics and hyperbilirubinemia. Clin. Cancer Res. 2012, 18, 2099–2107. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  68. Boudou-Rouquette, P.; Narjoz, C.; Golmard, J.L.; Thomas-Schoemann, A.; Mir, O.; Taieb, F.; Durand, J.-P.; Coriat, R.; Dauphin, A.; Vidal, M.; et al. Early sorafenib-induced toxicity is associated with drug exposure and UGTIA9 genetic polymorphism in patients with solid tumors: A preliminary study. PLoS ONE 2012, 7, e42875. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  69. Mizuno, T.; Fukudo, M.; Terada, T.; Kamba, T.; Nakamura, E.; Ogawa, O.; Inui, K.-I.; Katsura, T. Impact of genetic variation in breast cancer resistance protein (BCRP/ABCG2) on sunitinib pharmacokinetics. Drug Metab. Pharm. 2012, 27, 631–639. [Google Scholar] [CrossRef]
  70. Powles, T.; Plimack, E.R.; Soulières, D.; Waddell, T.; Stus, V.; Gafanov, R.; Nosov, D.; Pouliot, F.; Melichar, B.; Vynnychenko, I.; et al. Pembrolizumab plus axitinib versus sunitinib monotherapy as first-line treatment of advanced renal cell carcinoma (KEYNOTE-426): Extended follow-up from a randomised, open-label, phase 3 trial. Lancet Oncol. 2020, 21, 1563–1573. [Google Scholar] [CrossRef]
  71. Apolo, A.B.; Nadal, R.; Girardi, D.M.; Niglio, S.A.; Ley, L.; Cordes, L.M.; Steinberg, S.M.; Sierra Ortiz, O.; Cadena, J.; Diaz, C.; et al. Phase I Study of Cabozantinib and Nivolumab Alone or with Ipilimumab for Advanced or Metastatic Urothelial Carcinoma and Other Genitourinary Tumors. J. Clin. Oncol. 2020, 38, 3672–3684. [Google Scholar] [CrossRef]
  72. Arrondeau, J.; Mir, O.; Boudou-Rouquette, P.; Coriat, R.; Ropert, S.; Dumas, G.; Rodrigues, M.J.; Rousseau, B.; Blanchet, B.; Goldwasser, F. Sorafenib exposure decreases over time in patients with hepatocellular carcinoma. Investig. New Drugs 2012, 30, 2046–2049. [Google Scholar] [CrossRef]
Cancers 15 00313 g001 550
Figure 1. Analysis of pazopanib plasma concentration observed (●) at time 8 h after the last daily dose using mapbayr [60] in order to estimate Cmin,ss (o) and to compare it with the target value (i.e., 20 mg/L).
Figure 1. Analysis of pazopanib plasma concentration observed (●) at time 8 h after the last daily dose using mapbayr [60] in order to estimate Cmin,ss (o) and to compare it with the target value (i.e., 20 mg/L).
Cancers 15 00313 g001
Table
Table 1. Pharmacokinetics of tyrosine kinase inhibitors prescribed in metastatic renal cancer.
Table 1. Pharmacokinetics of tyrosine kinase inhibitors prescribed in metastatic renal cancer.
CL/F (L/h)
IIV (%)		F (%)Tmax (h)Unbound Plasma Fraction (%)Metabolic PathwaysDrug
Transporters		Elimination Half-Life (h)Ref
Sorafenib8.13 (18)ND3<1CYP3A4, UGT1A9MRP225–48[13]
Sunitinib46.4 (46)ND8.55CYP3A4BCRP40–60[14,15]
Pazopanib0.458 (71)21.53<0.1CYP3A4P-gp; BCRP27–36[16,17]
Axitinib32.3 (57)583.9–6.01CYP3A4, CYP1A2, CYP2C19, UGT1A1P-gp; BCRP 2.5–6.1 (43)[18]
Lenvatinib6.5 (25.5)ND1–41–2CYP3A4P-gp; BCRP20.6–34.3[19]
Cabozantinib2.2 (46)ND2–3<1CYP3A4, CYP2C9MRP2, OAT3120[20,21]
Tivozanib0.862 (men); 0.651 (women) (40.5)ND3<1CYP3A4, UGT?-108–121[22]
CL/F, oral clearance; F, oral bioavailability; Tmax, time of maximum plasma concentration; ND, not determined; CYP, cytochrome P450; UGT, UDP-glucuronosyl transferase; MRP, multidrug resistance-associated protein; BCRP, breast cancer-resistant protein; P-gp, P-glycoprotein; OAT, organic anion transporter.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Puisset, F.; Mseddi, M.; Mourey, L.; Pouessel, D.; Blanchet, B.; Chatelut, E.; Chevreau, C. Therapeutic Drug Monitoring of Tyrosine Kinase Inhibitors in the Treatment of Advanced Renal Cancer. Cancers 2023, 15, 313. https://doi.org/10.3390/cancers15010313

AMA Style

Puisset F, Mseddi M, Mourey L, Pouessel D, Blanchet B, Chatelut E, Chevreau C. Therapeutic Drug Monitoring of Tyrosine Kinase Inhibitors in the Treatment of Advanced Renal Cancer. Cancers. 2023; 15(1):313. https://doi.org/10.3390/cancers15010313

Chicago/Turabian Style

Puisset, Florent, Mourad Mseddi, Loïc Mourey, Damien Pouessel, Benoit Blanchet, Etienne Chatelut, and Christine Chevreau. 2023. "Therapeutic Drug Monitoring of Tyrosine Kinase Inhibitors in the Treatment of Advanced Renal Cancer" Cancers 15, no. 1: 313. https://doi.org/10.3390/cancers15010313

APA Style

Puisset, F., Mseddi, M., Mourey, L., Pouessel, D., Blanchet, B., Chatelut, E., & Chevreau, C. (2023). Therapeutic Drug Monitoring of Tyrosine Kinase Inhibitors in the Treatment of Advanced Renal Cancer. Cancers, 15(1), 313. https://doi.org/10.3390/cancers15010313

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop